Adaptive Variational Mode Time-frequency Analysis of Ship Radiated Noise

In this paper, we have proposed a novel time-frequency analysis method implementing adaptive variational mode decomposition (AVMD) and Wigner-Ville Distribution (WVD) to visualize the local features of ship radiated noise. Unlike the traditional methods are invalid under low signal to noise ratio (SNR), the proposed method is suitable for constructing time-frequency (TF) image with the capability of mining the local feature information accurately. Firstly, we employ complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) on the raw signal to determine the numbers of variational modes which is equal to intrinsic mode functions (IMFs) numbers. Secondly, we perform VMD algorithm to extract the local features absolutely by decomposing the raw signal into IMFs, where we set the IMFs gained by CEEMDAN as the input parameter of VMD. Finally, we adopt Wigner-Ville distribution (WVD) to draw TF signatures of all the IMFs, then superpose them to reconstruct the objected signal that can solve the cross-item problem. We have conducted the comparative experiments by the real oceanic data. Under the different SNRs, we have verified the effectiveness and robustness of the proposed method, also obtained better performance in terms of resolution than compared algorithm.